arXiv 2001.095422020-01-27astro-ph.EPastro-ph.IMphysics.comp-ph

机器学习应用于模拟旋转，差异化行星之间的碰撞

Machine learning applied to simulations of collisions between rotating, differentiated planets

Miles Timpe, Maria Han Veiga, Mischa Knabenhans, Joachim Stadel, Stefano Marelli

备注：: 28 pages, 8 figures. Submitted to Computational Astrophysics and Cosmology
DOI：: 10.1186/s40668-020-00034-6

摘要：

在类地行星形成的后期，行星大小的天体之间的成对碰撞是行星生长的基本因素。这些碰撞可能导致所涉及的天体的生长或分裂，并在很大程度上决定了行星的最终特征。尽管它们在行星形成中起着关键作用，但对碰撞的准确处理还有待实现。虽然已经提出了半分析方法，但它们仍然限于一组狭窄的撞击后特性，并且仅实现了相对较低的准确性。然而，机器学习的兴起和计算能力的提高使新的数据驱动方法成为可能。在这项工作中，我们表明，数据驱动的仿真技术能够预测碰撞的结果具有高精度，并可推广到任何可量化的影响后的数量。特别是，我们专注于数据集的要求，训练管道，和回归性能的四个不同的数据驱动的技术，从机器学习（集成方法和神经网络）和不确定性量化（高斯过程和多项式混沌扩展）。我们将这些方法与现有的解析和半解析方法进行比较。这种数据驱动的仿真器有望取代目前在N体模拟中使用的方法。这项工作是基于一组新的10,700 SPH模拟旋转，分化机构之间的成对碰撞在所有可能的相互方向。

英文摘要：

In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations. This work is based on a new set of 10,700 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

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